Correlating Architecture Maturity and Enterprise Systems Usage Maturity to Improve Business/IT Alignment

This paper compares concepts of maturity models in the areas of Enterprise Architecture and Enterprise Systems Usage. We investigate whether these concepts correlate, overlap and explain each other. The two maturity models are applied in a case study. We conclude that although it is possible to fully relate constructs from both kinds of models, having a mature architecture function in a company does not imply a high Enterprise Systems Usage maturity

Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure

Innovation in logistics services – halal logistics

Purpose: The expansion of liberalization of trade and services has forced companies to consider the global market demand in their competitive strategic planning. Hence, business organisations need to be continuously as innovation could promise potential growth and development so as to gain competitive advantage in being ahead in the market. Specifically, supply chain has always been viewed as the most important areas to be innovated as it would be an effective means to gain efficiencies and eliminate accumulating competitive pressures and thus increasing innovations. Thus, the purpose of this study is to present a case study that demonstrates an innovation created in the logistics services, i.e. halal logistics services, as to fulfil the increasing demand of the customers throughout the world particularly the rising number of Muslim population. Research approach: This study uses case study approach to elaborate the implementation of halal logistics practice. In achieving the objective, related literature concerning the halal concept is reviewed and explained to provide better understandings of the concept and how it is applied to logistics services. The emphasis on the innovativeness of this concept is also included. The data for the case study is gained from in-depth interviews with the corporate and operation managers of two leading logistics service providers in Malaysia, who are the subjects of the case. Findings and Originality: This study found new logistics services that are able to fulfil the growing demand of the customers especially the increasing number of Muslims. This is important as these services have taken into consideration several factors such as comprehensive hygiene practices and thus, is also crucial to other customers. However, understanding the basic concept of halal practice and the rationale of its implementation is very crucial before one can commit to its practice. This study contributes to the advancement of knowledge through the elaboration of a case study, which demonstrates the application of halal concept into logistics service practices. Research impact: This study introduces a new concept of halal logistics, which applies the concept of halal into logistics. The needs to initiate more logistics services that are based on halal concept are crucial in meeting the needs of the increasing demand by the customers. Practical impact: The findings provide insights to the practitioners of the importance in implementating halal logistics services. It also indicates the needs for logistics companies to be innovative in creating more halal logistics services to fulfill these demands.Halal logistics, innovation, case study

BUSINESS INTELLIGENT AGENTS FOR ENTERPRISE APPLICATION

Fierce competition in a market increasingly crowded and frequent changes in consumer requirements are the main forces that will cause companies to change their current organization and management. One solution is to move to open architectures and virtual type, which requires addressing business methods and technologies using distributed multi-agent systems. Intelligent agents are one of the most important areas of artificial intelligence that deals with the development of hardware and software systems able to reason, learn to recognize natural language, speak, make decisions, to recognize objects in the working environment etc. Thus in this paper, we presented some aspects of smart business, intelligent agents, intelligent systems, intelligent systems models, and I especially emphasized their role in managing business processes, which have become highly complex systems that are in a permanent change to meet the requirements of timely decision making. The purpose of this paper is to prove that there is no business without using the integration Business Process Management, Web Services and intelligent agents.business intelligence, intelligent agents, intelligent systems, management, enterprise, web services

A Bi-Directional Approach for Developing Data Warehouses in Public Sectors

Data warehouse is proclaimed as the latest decision support technology. As data warehouses require a significant amount of organizational resources to develop, more research have been devoted to identifying the critical success factors and the formulas for assured investment return from data warehouses. This study proposes a bi-directional development approach for data warehouses in public sectors. The primary rationale for the proposed approach is the fundamentally different organizational goals of public sector organizations from private sector organizations. Whereas the ultimate goal of private sector organizations is profit making, public sector organizations have a set of conflicting goals including different social and political objectives. The star schema as a dimensional data model for data warehouse is not totally suitable for data warehouses that demand the analyses of both quantitative and qualitative measures. Using the data warehouse in the College of Business Administration at the California State University, Sacramento as a case study, we illustrate how the QQ (Quantitative and Qualitative) data schema accommodates the need of capturing both quantitative and qualitative information. In addition, we show the bidirectional top-down/bottom-up initiative, the formal/informal information collection, and the enterprise data warehouse/subject data mart architecture for the data warehouse

THE ROLE OF DATA ARCHITECTURE AS A PART OF ENTERPRISE ARCHITECTURE

In the early days of computing, technology simply automated manual processes with greater efficiency. The new organizational context provides input into the data architecture and is the primary tool for the management and sharing of enterprise data. It enables architects, data modelers, and stakeholders to identify, classify, and analyze information requirements across the enterprise, allowing the right priorities for data sharing initiatives. Data architecture states how data are persisted, managed, and utilized within an organization. Data architecture is made up of the structure of all corporate data and its relationships to itself and external systems. In far too many situations, the business community has to enlist the assistance of IT to retrieve information due to the community's inconsistency, lack of intuitiveness, or other factors. The goal of any architecture should illustrate how the components of the architecture will fit together and how the system will adapt and evolve over time.data architecture, enterprise architecture, business process planning,databases, business objects.

Impact of service-oriented architectures (SOA) on business process standardization - Proposing a research model

Originally, Data Warehouses (DWH) were conceived to be components for the data support of controlling and management. From early on, this brought along the need to cope with extensive data preparation, integration, and distribution requirements. In the growing infrastructures for managerial support (“Business Intelligence”), the DWH turned into a central data hub for decision support. As the business environment and the underlying technical infrastructures are fostering an ever increasing degree of systems integration, the DWH has been recognized to be a pivotal component for all sorts of data transformation and data integration operations. Nowadays, the DWH is supposed to process both managerial and operational data – it becomes a transformation hub (TH). This article delineates the relevant motives that drive the trend towards THs and the resulting requirements. The logical composition of a TH is developed based on data transformation steps. Two case studies exemplify the application of the resulting architecture

Review of modern business intelligence and analytics in 2015: How to tame the big data in practice?: Case study - What kind of modern business intelligence and analytics strategy to choose?

The objective of this study was to find out the state of art architecture of modern business intelligence and analytics. Furthermore the status quo of business intelligence and analytics' architecture in an anonymous case company was examined. Based on these findings a future strategy was designed to guide the case company towards a better business intelligence and analytics environment. This objective was selected due to an increasing interest on big data topic. Thus the understanding on how to move on from traditional business intelligence practices to modern ones and what are the available options were seen as the key questions to be solved in order to gain competitive advantage for any company in near future. The study was conducted as a qualitative single-case study. The case study included two parts: an analytics maturity assessment, and an analysis of business intelligence and analytics' architecture. The survey included over 30 questions and was sent to 25 analysts and other individuals who were using a significant time to deal with or read financial reports like for example managers. The architecture analysis was conducted by gathering relevant information on high level. Furthermore a big picture was drawn to illustrate the architecture. The two parts combined were used to construct the actual current maturity level of business intelligence and analytics in the case company. Three theoretical frameworks were used: first framework regarding the architecture, second framework regarding the maturity level and third framework regarding reporting tools. The first higher level framework consisted of the modern data warehouse architecture and Hadoop solution from D'Antoni and Lopez (2014). The second framework included the analytics maturity assessment from the data warehouse institute (2015). Finally the third framework analyzed the advanced analytics tools from Sallam et al. (2015). The findings of this study suggest that modern business intelligence and analytics solution can include both data warehouse and Hadoop components. These two components are not mutually exclusive. Instead Hadoop is actually augmenting data warehouse to another level. This thesis shows how companies can evaluate their current maturity level and design a future strategy by benchmarking their own actions against the state of art solution. To keep up with the fast pace of development, research must be continuous. Therefore in future for example a study regarding a detailed path of implementing Hadoop would be a great addition to this field